Constraint handling procedure for multiobjective particle swarm optimization

In this paper, the proposed constrained multiobejctive particle swarm optimization (MOPSO) adopts the multiobjective constraint handling framework and includes the following design features: An infeasible global best archive to guide the infeasible particles towards feasible region(s); procedures to update the personal best archive are designed to encourage finding feasible regions and convergence towards the Pareto front; acceleration constants in the particle swarm optimization equation are adjusted during the search process to encourage finding more feasible particles or to search for better solutions; and mutation operators are adopted to encourage global and local searches. The simulation results indicate that the proposed algorithm is highly competitive in solving the benchmark problems.